Bi-level Optimization Model of Day-ahead Demand Response Strategy for Load Aggregator

Load aggregators (LAs) play a coordinating role between the independent system operator and retail customers, which evaluate the regulatory value of small or medium users to achieve an efficient integration of discrete demand response resources. Dealing with the fluctuating electricity price of the day-ahead market, we established a bi-level optimization model of day-ahead demand response strategy for the load aggregator. Firstly, considering interaction constraints between the load aggregator and power users, a basic structure of a bi-level optimization scheme for incentive policies is proposed. Secondly, taking user-side uncertainty into account, with economic benefits and comfort effects considered, a lower rational user response model is established based on stochastic programming theory. Moreover, an upper load aggregator decision-making model to maximize the comprehensive benefits of demand response is proposed. Then, the model solution flow based on the differential evolution (DE) algorithm is given. Finally, the effectiveness of the model is verified by the analysis of different scenarios.